code: def init_hidden(self, batch_size):
      weight = next(self.parameters()).data
      zeros = Variable(
          weight.new(self.depth, batch_size, self.n_d).zero_()
      )
      if self.args.lstm:
          return (zeros, zeros)
      else:
          return zeros
  
imports: import sys
  import os
  import argparse
  import time
  import random
  import math
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
  import sru
label_desc: Use image node_id 4 for calling the Model obj's underlying member method code with example usage: obj.init_hidden(batch_size) and returns: zeros,  zeros, zeros